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在线训练专属LoRA模型,无需下载,即刻生成
Loras AI 让你在线训练图像和视频模型的 LoRA,只需上传 4–30 张图片或 3–10 段视频,即可微调 Krea 2、FLUX、Qwen Image、Z-Image 等模型,无需本地 GPU 或下载。
“训练你的 LoRA,在线生成,无需下载”
平台提供字幕编辑器、触发词设置、My LoRAs 进度跟踪,并按任务消耗积分,成本透明。支持角色、产品和风格三种预设,训练后可直接在兼容生成器中使用。
| 功能 | Loras AI | 本地训练 |
|---|---|---|
| 需要 GPU | 否 | 是 |
| 需要编码 | 否 | 环境配置 |
| 训练后生成 | 同一工作区 | 需单独设置 |
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Maker
Loras AI is an online platform for training LoRAs — small, trained add-ons that teach an AI model a specific character, product or visual style from your own examples. Instead of setting up a local environment, you upload a focused dataset, fine-tune a supported base model in the cloud, and then generate new scenes with the result in the same workspace. There is no local GPU, no software installation and no model download involved.
A LoRA can be trained from 4–30 images, depending on the model, so you don't need a large archive to get started. Each image and caption can be reviewed before training begins.
Fine-tuning is available for image models such as Krea 2, FLUX.1 Dev, FLUX.2 Dev, Qwen Image 2512 and Z-Image Turbo, plus video families including Wan 2.2 and LTX-2 19B. Each LoRA must be used with its paired generator, since adapters are not interchangeable between base models.
A built-in caption editor lets you check every caption and set the trigger word before you commit to a training run. This keeps the dataset tidy without reaching for separate annotation tools.
My LoRAs tracks training progress and stores finished models for reuse. Costs are shown in credits before you submit a task, so you know what a run will take.
Train your LoRA, generate with it, all without a download or a local GPU.
That single-workspace flow is the real differentiator: dataset tools, training and generation live in one place, rather than being stitched together from separate scripts and setups. Credit costs are displayed up front, which makes the pay-per-task model easy to reason about. The platform is also available in English, 中文, 日本語 and 한국어.
You want custom, fine-tuned image or video models but don't have the hardware or the patience for local environment setup. It suits anyone who values a clear, step-by-step path — upload, fine-tune, create — over maximum configurability. If you already know which base model you plan to generate with, matching it to a compatible LoRA here is a straightforward next step.
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